On March 8, 2016, on the occasion of International Women’s Day, Habitat for Humanity International launched its first global advocacy campaign, “Solid Ground,” which envisions a world where everyone has access to land for shelter. Promoting gender equality and addressing inequitable or unenforced laws, policies, and customary practices affecting women’s rights to security of tenure and inheritance, has been a primary focus of the campaign.

Now mid-way through the campaign, Solid Ground has grown to include 37 national Habitat for Humanity organizations, 17 partner organizations, an active microsite solidgroundcampaign.org (and in Spanish, SueloUrbano.org), and has provided direct financial assistance to country programs working on gender and land issues. In its first year, over 1.3 million people are projected to have improved access to land for shelter through the Solid Ground campaign with a goal of reaching 10 million people, especially women.

In the previous blog, we discussed how remote sensing techniques could be used to map and inform policymaking in secondary cities, with a practical application in 10 Central American cities. In this post, we dive deeper into the caveats and considerations when replicating these data and methods in their cities.

Can we rely only on satellite? How accurate are these results?

It is standard practice in classification studies (particularly academic ones) to assess accuracy from behind a computer. Analysts traditionally pick a random selection of points and visually inspect the classified output with the raw imagery. However, these maps are meant to be left in the hands of local governments, and not published in academic journals.

So, it’s important to learn how well the resulting maps reflect the reality on the ground.

Having used the algorithm to classify land cover in 10 secondary cities in Central America, we were determined to learn if the buildings identified by the algorithm were in fact ‘industrial’ or ‘residential’. So the team packed their bags for San Isidro, Costa Rica and Santa Ana, El Salvador.

Upon arrival, each city was divided up into 100x100 meter blocks. Focusing primarily on the built-up environment, roughly 50 of those blocks were picked for validation. The image below shows the city of San Isidro with a 2km buffer circling around its central business district. The black boxes represent the validation sites the team visited.

Land Cover validation: A sample of 100m blocks that were picked to visit in San Isidro, Costa Rica. At each site, the semi-automated land cover classification map was compared to what the team observed on the ground using laptops and the Waypoint mobile app (available for Android and iOS).

The buzz around satellite imagery over the past few years has grown increasingly loud. Google Earth, drones, and microsatellites have grabbed headlines and slashed price tags. Urban planners are increasingly turning to remotely sensed data to better understand their city.

But just because we now have access to a wealth of high resolution images of a city does not mean we suddenly have insight into how that city functions.

In an effort a few years ago to map slums, the World Bank adopted an algorithm to create land cover classification layers in large African cities using very high resolution imagery (50cm). Building on the results and lessons learned, the team saw an opportunity in applying these methods to secondary cities in Latin America & the Caribbean (LAC), where data availability challenges were deep and urbanization pressures large. Several Latin American countries including Argentina, Bolivia, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama were faced with questions about the internal structure of secondary cities and had no data on hand to answer such questions.

A limited budget and a tight timeline pushed the team to assess the possibility of using lower resolution images compared to those that had been used for large African cities. Hence, the team embarked in the project to better understand the spatial layout of secondary cities by purchasing 1.5 meter SPOT6/7 imagery and using a semi-automated classification approach to determine what types of land cover could be successfully detected.

Originally developed by Graesser et al 2012 this approach trains (open source) algorithm to leverage both the spectral and texture elements of an image to identify such things as industrial parks, tightly packed small rooftops, vegetation, bare soil etc.

What do the maps look like? The figure below shows the results of a classification in Chinandega, Nicaragua. On the left hand side is the raw imagery and the resulting land cover map (i.e. classified layer) on the right. The land highlighted by purple shows the commercial and industrial buildings, while neighborhoods composed of smaller, possibly lower quality houses are shown in red, and neighborhoods with slightly larger more organized houses have been colored yellow. Lastly, vegetation is shown as green; bare soil, beige; and roads, gray.

Because violence affects everyone, it takes us all—from individuals to communities, and from cities to countries—to tackle the pandemic of violence against our women and girls.

On Day 15 of the global #16Days campaign, let’s take a look at a few examples of how community groups, civil society organizations, and national governments around the world are making informed efforts to prevent and respond to various forms of gender-based violence.

The city of La Paz in Bolivia is piloting a new tool called Barrio Digital—or Digital Neighborhood—to communicate more effectively and efficiently with citizens living in areas that fall within Barrios de Verdad, or PBCV, an urban upgrading program that provides better services and living conditions to people in poor neighborhoods.

The goals of Barrio Digital are to:

Increase citizen participation for evidence-based decision-making,

Reduce the cost of submitting a claim and shorten the amount of time it takes for the municipality to respond, and

Strengthen the technical skills and capacity within the municipality to use ICT tools for citizen engagement.

I was with the World Bank delegation at the Habitat III Conference in Quito last week, reflecting on the future of cities and speaking at a panel on food security. While there, I could not help but remember the story of Wara, an indigenous Aymara woman, one of eight children from a poor rural family living in the Bolivian Altiplano. Poverty forced her to migrate to the city when she was young.

Now living in La Paz, Wara has been working as a nanny in households for decades. She has three teenagers. Her oldest son is overweight and has already had several health problems. He occasionally works with his father building houses. The other kids are still in school and Wara hopes that armed with an education, they will be able to find a good job.

According to statistics, Wara is no longer poor. Indeed, Wara and her family are better off when compared to her modest origins. The truth is, however, that she is vulnerable and can easily fall back into poverty and hunger.

As in most Aymara families, Wara’s husband administers the money, including her own earnings, but she is the food-provider for the family. Each Saturday he gives Wara some money to get food for the week. She wakes up early to go to one of the four big markets in La Paz to buy basic staples such as potatoes, fresh vegetables, rice, sugar and oil, among others.

Photo: Pierre-Yves Babelon/Shutterstock
In an effort to harness the benefits of urbanization and improve the living conditions of the urban poor, Latin American countries have experimented with housing subsidies. Now that the region has several decades of experience under its belt, it is time to look back and ask: Have subsidies worked? What kind of impact have they had on the lives of lower-income residents? Moving forward, how can cities pay for ongoing urban renewal?

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